1. Field of the Invention
The present invention relates to road shape recognition devices, and specifically relates to a road shape recognition device which generates a road shape model based on distance data obtained by distance and height detecting means.
2. Description of the Related Art
In recent years, development of road shape recognition devices has been underway, in which an image capturing device such as a Charge Coupled Device (CCD) camera and Complementary Metal Oxide Semiconductor (CMOS) camera, or a laser radar distance measuring device, is mounted on a vehicle, and a road shape where the vehicle equipped with such a device is traveling is recognized by image analysis of an image captured with the image capturing means, or by reflected light analysis of radio waves or a laser beam emitted from the laser radar distance measuring device, or the like (e.g., see Japanese Unexamined Patent Application Publication (JP-A) Nos. 1994-266828, 2001-092970 and 2001-227944).
JP-A No. 1994-266828 discloses a technique wherein image capturing means comprised of a stereo camera or a laser radar distance measuring device is used, and distance data in real space up to an object in front of such a device is detected based on the triangulation principle, or by reflected light analysis of a laser beam or the like. Subsequently, of the distance data thereof, distance data included in a window, which is set by three-dimensional or two-dimensional projection based on a road shape model detected at a last sampling cycle, is subjected to linear approximation for each section set in the front of a vehicle, and road shape models in the horizontal direction and in the vertical direction are calculated.
In addition, JP-A No. 2001-092970 discloses a technique wherein, of a pair of images captured with image capturing means comprised of a stereo camera, pixels corresponding to a lane line lateral to a vehicle are detected out of the image based on the luminance of each pixel of one of the images, the distance data of the pixels corresponding to the detected lane line is subjected to linear approximation for each section set in the front of the vehicle, and road shape models in the horizontal direction and in the vertical direction are calculated. Note that the “lane line” means a continuous line or dashed line marked on a road surface such as a no-passing line, a dividing line which divides a side strip and a roadway.
Further, in JP-A No. 2001-227944, description is made wherein an image capturing device comprised of a stereo camera is used, and with regard to the horizontal direction, i.e., a direction where a traveling route curves, a road shape is approximated by a quadratic function, while with regard to the vertical direction, the slope angle of a road surface is calculated from engine output and acceleration at the time of the vehicle traveling.
The road shapes, particularly a road shape in the vertical direction (height direction) can be modeled by subjecting the distance data of pixels corresponding to a detected lane line to linear approximation for each section in front of a vehicle, as described in JP-A No. 2001-092970 or the like. However, processing load may be large when dividing a region in front of the vehicle into a great number of sections and performing linear approximation for each section.
Therefore, in actual practice, in order to simplify and alleviate the processing load, a region in front of the vehicle is often divided into two sections of the near side and the far side of the vehicle with a fixed distance from the vehicle as a boundary, and regarding the two sections, distance data up to an object obtained by image analysis of an captured image, reflected light analysis using a laser radar distance measuring device, or the like, is subjected to linear approximation to calculate a road shape model.
For example, when a road where a vehicle is traveling has an uphill A in the distance as shown in FIG. 18A, by taking the height direction (vertical direction) as the Y axis and the distance direction (the forward direction of the vehicle) as the Z axis, distance data is plotted on a Z-Y plane as shown in FIG. 18B. Subsequently, for example, when sections R1 and R2 are divided with a position as a boundary that is distant from the vehicle located at the position of Z=0 by a fixed distance Z0, distance data of the sections R1 and R2 is subjected to linear approximation as shown in FIG. 18B, and a road shape model is generated.
In this case, the distance data in the sections R1 and R2 are approximated by, for example, approximation lines L1 and L2, and distance data corresponding to the distant uphill A exists above the approximation line L2. Accordingly, the distance data corresponding to the uphill A may accidentally satisfy a condition for detecting a three-dimensional object, and the portion of the uphill A may be erroneously detected as a three-dimensional object existing on a road surface.
Moreover, when a road where a vehicle is traveling has a downhill B in the distance as shown in FIG. 19A and distance data is plotted in the same way as with FIG. 18B, the distance data is plotted on a Z-Y plane as shown in FIG. 19B. Subsequently, the distance data of the sections R1 and R2 divided with the position of the distance Z0 as a boundary, are approximated by the approximation lines L1 and L2 respectively, and a road shape model is generated.
In this case, when a preceding vehicle Vah is traveling on the road surface of the downhill B as shown in FIG. 19A, the distance data of the back portion of the preceding vehicle Vah is detected on the upper side of the distance data of the downhill B, as shown in FIG. 19B. However, as shown in FIG. 19B, in a situation in which the back portion of the preceding vehicle Vah exists on an extension of the flat road surface in front of the downhill B, at the time of performing linear approximation using the section R2, the distance data of the preceding vehicle Vah is not distinguished from the distance data of the road surface and is subjected to linear approximation, and consequently the preceding vehicle Vah may be erroneously detected as a road surface.
In the event that the above erroneous detection of a three-dimensional object or preceding vehicle Vah occurs, for example, automatic control of the vehicle performed based on the information thereof may be different from a driver's intention. In addition, as shown in FIGS. 18B and 19B, in the case that a region in front the vehicle is divided into sections R1 and R2 with the position far from the vehicle by the fixed distance Z0 as a boundary, and is subjected to linear approximation, the approximation lines L1 and L2, i.e., a road shape model is not necessarily a model appropriately representing a real road shape.
A possible solution of this problem is to set the position of a boundary for dividing a region in front of the vehicle (the position of the distance Z0 in the above example) not fixedly but variably, calculate the approximation lines L1 and L2 for the divided sections R1 and R2 respectively, and search the most appropriate boundary position and approximation lines L1 and L2. However, if processing thereof takes time, it is difficult to apply this processing to an automatic control technique of the vehicle, or the like.